The ability to revise in response to feedback is critical to students' writing success. In the case of argument writing in specific, identifying whether an argument revision (AR) is successful or not is a complex problem because AR quality is dependent on the overall content of an argument. For example, adding the same evidence sentence could strengthen or weaken existing claims in different argument contexts (ACs). To address this issue we developed Chain-of-Thought prompts to facilitate ChatGPT-generated ACs for AR quality predictions. The experiments on two corpora, our annotated elementary essays and existing college essays benchmark, demonstrate the superiority of the proposed ACs over baselines.

翻译：根据反馈进行修订的能力对于学生的写作成功至关重要。特别是在议论文写作中，判断一项论证修订（AR）是否成功是一个复杂问题，因为修订质量取决于论证的整体内容。例如，在相同的证据句子加入后，其在不同论证上下文（ACs）中可能增强或削弱已有的论点。为解决此问题，我们开发了思维链提示，以促进ChatGPT生成用于预测AR质量的论证上下文。在两个语料库（我们标注的小学作文和现存的大学作文基准）上的实验表明，所提出的ACs方法优于基线方法。